Comprehensive evaluation of protein-coding sORFs prediction based on a random sequence strategy

Yu, Jiafeng; Guo, Li; Dou, Xianghua; Jiang, Wenwen; Qian, Bowen; Liu, Jian; Wang, Jun; Xu, Congmin

doi:10.52586/4943

Cited by 6 publications

(5 citation statements)

References 39 publications

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“…Third, we still do not have a good negative dataset for predicting SPs. Most existing tools use noncoding RNAs as negatives, which are shown to potentially contain SPs [ 27 , 32 ]. Some studies use microRNAs, snRNAs, etc., which may be better than the above noncoding negatives but may not represent all negative SPs well.…”

Section: Discussionmentioning

confidence: 99%

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A survey of experimental and computational identification of small proteins

Beals,

Hu,

2024

Briefings in Bioinformatics

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Small proteins (SPs) are typically characterized as eukaryotic proteins shorter than 100 amino acids and prokaryotic proteins shorter than 50 amino acids. Historically, they were disregarded because of the arbitrary size thresholds to define proteins. However, recent research has revealed the existence of many SPs and their crucial roles. Despite this, the identification of SPs and the elucidation of their functions are still in their infancy. To pave the way for future SP studies, we briefly introduce the limitations and advancements in experimental techniques for SP identification. We then provide an overview of available computational tools for SP identification, their constraints, and their evaluation. Additionally, we highlight existing resources for SP research. This survey aims to initiate further exploration into SPs and encourage the development of more sophisticated computational tools for SP identification in prokaryotes and microbiomes.

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Section: Discussionmentioning

confidence: 99%

“…PsORF is a protein-coding sORF prediction tool specifically designed for prokaryotic sORFs [ 32 ]. The tool is written in Matlab and is no longer accessible.…”

Section: Tools For Small Protein Identification In Prokaryotesmentioning

confidence: 99%

A survey of experimental and computational identification of small proteins

Beals,

Hu,

2024

Briefings in Bioinformatics

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“…We collected prokaryotic SPs from three sources. First, we extracted data from the prokaryotic dataset Pro-6318 by Yu et al (2021) . This dataset comprises 6,318 sORFs from 56 prokaryotic species, with average and median lengths of 76 and 78 AA, respectively.…”

Section: Methodsmentioning

confidence: 99%

“…A handful of computational methods have been developed for predicting SPs without additional experimental data ( Miravet-Verde et al, 2019 ; Zhu and Gribskov, 2019 ; Durrant and Bhatt, 2021 ; Yu et al, 2021 ; Zhang et al, 2021 ; Zhang et al, 2022 ). Most of these methods are created to target SPs or sORFs in eukaryotes, such as csORF-Finder, MiPepid, and DeepCPP.…”

Section: Introductionmentioning

confidence: 99%

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PSPI: A deep learning approach for prokaryotic small protein identification

Weston,

Hu,

2024

Front. Genet.

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Small Proteins (SPs) are pivotal in various cellular functions such as immunity, defense, and communication. Despite their significance, identifying them is still in its infancy. Existing computational tools are tailored to specific eukaryotic species, leaving only a few options for SP identification in prokaryotes. In addition, these existing tools still have suboptimal performance in SP identification. To fill this gap, we introduce PSPI, a deep learning-based approach designed specifically for predicting prokaryotic SPs. We showed that PSPI had a high accuracy in predicting generalized sets of prokaryotic SPs and sets specific to the human metagenome. Compared with three existing tools, PSPI was faster and showed greater precision, sensitivity, and specificity not only for prokaryotic SPs but also for eukaryotic ones. We also observed that the incorporation of (n, k)-mers greatly enhances the performance of PSPI, suggesting that many SPs may contain short linear motifs. The PSPI tool, which is freely available at https://www.cs.ucf.edu/∼xiaoman/tools/PSPI/, will be useful for studying SPs as a tool for identifying prokaryotic SPs and it can be trained to identify other types of SPs as well.

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Prediction of protein-coding small ORFs in multi-species using integrated sequence-derived features and the random forest model

Jiang

Zhu

et al. 2023

Methods

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Comprehensive evaluation of protein-coding sORFs prediction based on a random sequence strategy

Cited by 6 publications

References 39 publications

A survey of experimental and computational identification of small proteins

A survey of experimental and computational identification of small proteins

PSPI: A deep learning approach for prokaryotic small protein identification

Prediction of protein-coding small ORFs in multi-species using integrated sequence-derived features and the random forest model

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